Overview

Brought to you by YData

Dataset statistics

Number of variables42
Number of observations494021
Missing cells0
Missing cells (%)0.0%
Duplicate rows19377
Duplicate rows (%)3.9%
Total size in memory259.0 MiB
Average record size in memory549.7 B

Variable types

Numeric28
Categorical13
Text1

Alerts

num_outbound_cmds has constant value "0" Constant
is_host_login has constant value "0" Constant
Dataset has 19377 (3.9%) duplicate rowsDuplicates
count is highly overall correlated with dst_bytes and 9 other fieldsHigh correlation
diff_srv_rate is highly overall correlated with dst_host_diff_srv_rate and 10 other fieldsHigh correlation
dst_bytes is highly overall correlated with count and 3 other fieldsHigh correlation
dst_host_count is highly overall correlated with count and 3 other fieldsHigh correlation
dst_host_diff_srv_rate is highly overall correlated with count and 11 other fieldsHigh correlation
dst_host_rerror_rate is highly overall correlated with dst_host_srv_rerror_rate and 2 other fieldsHigh correlation
dst_host_same_src_port_rate is highly overall correlated with count and 12 other fieldsHigh correlation
dst_host_same_srv_rate is highly overall correlated with count and 12 other fieldsHigh correlation
dst_host_serror_rate is highly overall correlated with diff_srv_rate and 9 other fieldsHigh correlation
dst_host_srv_count is highly overall correlated with count and 12 other fieldsHigh correlation
dst_host_srv_diff_host_rate is highly overall correlated with dst_bytes and 1 other fieldsHigh correlation
dst_host_srv_rerror_rate is highly overall correlated with dst_host_rerror_rate and 2 other fieldsHigh correlation
dst_host_srv_serror_rate is highly overall correlated with diff_srv_rate and 9 other fieldsHigh correlation
hot is highly overall correlated with is_guest_login and 1 other fieldsHigh correlation
is_guest_login is highly overall correlated with hotHigh correlation
land is highly overall correlated with outcomeHigh correlation
logged_in is highly overall correlated with count and 3 other fieldsHigh correlation
num_access_files is highly overall correlated with su_attemptedHigh correlation
num_compromised is highly overall correlated with hot and 1 other fieldsHigh correlation
num_root is highly overall correlated with su_attemptedHigh correlation
outcome is highly overall correlated with land and 4 other fieldsHigh correlation
protocol_type is highly overall correlated with count and 6 other fieldsHigh correlation
rerror_rate is highly overall correlated with dst_host_rerror_rate and 2 other fieldsHigh correlation
root_shell is highly overall correlated with outcomeHigh correlation
same_srv_rate is highly overall correlated with diff_srv_rate and 10 other fieldsHigh correlation
serror_rate is highly overall correlated with diff_srv_rate and 9 other fieldsHigh correlation
src_bytes is highly overall correlated with count and 11 other fieldsHigh correlation
srv_count is highly overall correlated with count and 8 other fieldsHigh correlation
srv_diff_host_rate is highly overall correlated with dst_bytesHigh correlation
srv_rerror_rate is highly overall correlated with dst_host_rerror_rate and 2 other fieldsHigh correlation
srv_serror_rate is highly overall correlated with diff_srv_rate and 9 other fieldsHigh correlation
su_attempted is highly overall correlated with num_access_files and 2 other fieldsHigh correlation
wrong_fragment is highly overall correlated with outcomeHigh correlation
flag is highly imbalanced (71.1%) Imbalance
land is highly imbalanced (99.9%) Imbalance
wrong_fragment is highly imbalanced (98.3%) Imbalance
urgent is highly imbalanced (> 99.9%) Imbalance
root_shell is highly imbalanced (99.8%) Imbalance
su_attempted is highly imbalanced (> 99.9%) Imbalance
num_shells is highly imbalanced (99.9%) Imbalance
is_guest_login is highly imbalanced (98.5%) Imbalance
outcome is highly imbalanced (65.6%) Imbalance
duration is highly skewed (γ1 = 25.86485736) Skewed
src_bytes is highly skewed (γ1 = 699.213151) Skewed
dst_bytes is highly skewed (γ1 = 136.7592782) Skewed
hot is highly skewed (γ1 = 32.62914514) Skewed
num_failed_logins is highly skewed (γ1 = 160.8026164) Skewed
num_compromised is highly skewed (γ1 = 417.5302281) Skewed
num_root is highly skewed (γ1 = 417.0658359) Skewed
num_file_creations is highly skewed (γ1 = 192.3347657) Skewed
num_access_files is highly skewed (γ1 = 61.20145172) Skewed
duration has 481671 (97.5%) zeros Zeros
src_bytes has 115342 (23.3%) zeros Zeros
dst_bytes has 408258 (82.6%) zeros Zeros
hot has 490829 (99.4%) zeros Zeros
num_failed_logins has 493958 (> 99.9%) zeros Zeros
num_compromised has 491797 (99.5%) zeros Zeros
num_root has 493436 (99.9%) zeros Zeros
num_file_creations has 493756 (99.9%) zeros Zeros
num_access_files has 493567 (99.9%) zeros Zeros
serror_rate has 404787 (81.9%) zeros Zeros
srv_serror_rate has 405686 (82.1%) zeros Zeros
rerror_rate has 464948 (94.1%) zeros Zeros
srv_rerror_rate has 464320 (94.0%) zeros Zeros
diff_srv_rate has 382021 (77.3%) zeros Zeros
srv_diff_host_rate has 459377 (93.0%) zeros Zeros
dst_host_same_srv_rate has 11469 (2.3%) zeros Zeros
dst_host_diff_srv_rate has 347031 (70.2%) zeros Zeros
dst_host_same_src_port_rate has 142860 (28.9%) zeros Zeros
dst_host_srv_diff_host_rate has 441889 (89.4%) zeros Zeros
dst_host_serror_rate has 399810 (80.9%) zeros Zeros
dst_host_srv_serror_rate has 400945 (81.2%) zeros Zeros
dst_host_rerror_rate has 458792 (92.9%) zeros Zeros
dst_host_srv_rerror_rate has 459805 (93.1%) zeros Zeros

Reproduction

Analysis started2024-10-22 14:00:04.306085
Analysis finished2024-10-22 14:04:22.594393
Duration4 minutes and 18.29 seconds
Software versionydata-profiling vv4.11.0
Download configurationconfig.json

Variables

duration
Real number (ℝ)

Skewed  Zeros 

Distinct2495
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.979302
Minimum0
Maximum58329
Zeros481671
Zeros (%)97.5%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2024-10-22T19:34:22.946930image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum58329
Range58329
Interquartile range (IQR)0

Descriptive statistics

Standard deviation707.74647
Coefficient of variation (CV)14.751079
Kurtosis942.53024
Mean47.979302
Median Absolute Deviation (MAD)0
Skewness25.864857
Sum23702783
Variance500905.07
MonotonicityNot monotonic
2024-10-22T19:34:23.107231image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 481671
97.5%
1 2476
 
0.5%
2 870
 
0.2%
3 625
 
0.1%
5 554
 
0.1%
2630 496
 
0.1%
4 413
 
0.1%
14 322
 
0.1%
10 194
 
< 0.1%
7 169
 
< 0.1%
Other values (2485) 6231
 
1.3%
ValueCountFrequency (%)
0 481671
97.5%
1 2476
 
0.5%
2 870
 
0.2%
3 625
 
0.1%
4 413
 
0.1%
5 554
 
0.1%
6 157
 
< 0.1%
7 169
 
< 0.1%
8 103
 
< 0.1%
9 121
 
< 0.1%
ValueCountFrequency (%)
58329 1
< 0.1%
42448 1
< 0.1%
42088 1
< 0.1%
41065 1
< 0.1%
40929 1
< 0.1%
40806 1
< 0.1%
40682 1
< 0.1%
40571 1
< 0.1%
40448 1
< 0.1%
40339 1
< 0.1%

protocol_type
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size28.5 MiB
icmp
283602 
tcp
190065 
udp
 
20354

Length

Max length4
Median length4
Mean length3.5740687
Min length3

Characters and Unicode

Total characters1765665
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowtcp
2nd rowtcp
3rd rowtcp
4th rowtcp
5th rowtcp

Common Values

ValueCountFrequency (%)
icmp 283602
57.4%
tcp 190065
38.5%
udp 20354
 
4.1%

Length

2024-10-22T19:34:23.268900image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-22T19:34:23.454933image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
icmp 283602
57.4%
tcp 190065
38.5%
udp 20354
 
4.1%

Most occurring characters

ValueCountFrequency (%)
p 494021
28.0%
c 473667
26.8%
i 283602
16.1%
m 283602
16.1%
t 190065
 
10.8%
u 20354
 
1.2%
d 20354
 
1.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1765665
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
p 494021
28.0%
c 473667
26.8%
i 283602
16.1%
m 283602
16.1%
t 190065
 
10.8%
u 20354
 
1.2%
d 20354
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1765665
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
p 494021
28.0%
c 473667
26.8%
i 283602
16.1%
m 283602
16.1%
t 190065
 
10.8%
u 20354
 
1.2%
d 20354
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1765665
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
p 494021
28.0%
c 473667
26.8%
i 283602
16.1%
m 283602
16.1%
t 190065
 
10.8%
u 20354
 
1.2%
d 20354
 
1.2%
Distinct66
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.4 MiB
2024-10-22T19:34:23.787772image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length11
Median length5
Mean length5.3699721
Min length3

Characters and Unicode

Total characters2652879
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowhttp
2nd rowhttp
3rd rowhttp
4th rowhttp
5th rowhttp
ValueCountFrequency (%)
ecr_i 281400
57.0%
private 110893
 
22.4%
http 64293
 
13.0%
smtp 9723
 
2.0%
other 7237
 
1.5%
domain_u 5863
 
1.2%
ftp_data 4721
 
1.0%
eco_i 1642
 
0.3%
ftp 798
 
0.2%
finger 670
 
0.1%
Other values (56) 6781
 
1.4%
2024-10-22T19:34:24.244168image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 405384
15.3%
i 402706
15.2%
r 401758
15.1%
_ 295947
11.2%
c 284019
10.7%
t 270911
10.2%
p 193674
7.3%
a 127712
 
4.8%
v 110999
 
4.2%
h 72835
 
2.7%
Other values (28) 86934
 
3.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2652879
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 405384
15.3%
i 402706
15.2%
r 401758
15.1%
_ 295947
11.2%
c 284019
10.7%
t 270911
10.2%
p 193674
7.3%
a 127712
 
4.8%
v 110999
 
4.2%
h 72835
 
2.7%
Other values (28) 86934
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2652879
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 405384
15.3%
i 402706
15.2%
r 401758
15.1%
_ 295947
11.2%
c 284019
10.7%
t 270911
10.2%
p 193674
7.3%
a 127712
 
4.8%
v 110999
 
4.2%
h 72835
 
2.7%
Other values (28) 86934
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2652879
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 405384
15.3%
i 402706
15.2%
r 401758
15.1%
_ 295947
11.2%
c 284019
10.7%
t 270911
10.2%
p 193674
7.3%
a 127712
 
4.8%
v 110999
 
4.2%
h 72835
 
2.7%
Other values (28) 86934
 
3.3%

flag
Categorical

Imbalance 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size27.8 MiB
SF
378440 
S0
87007 
REJ
 
26875
RSTR
 
903
RSTO
 
579
Other values (6)
 
217

Length

Max length6
Median length2
Mean length2.0605055
Min length2

Characters and Unicode

Total characters1017933
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSF
2nd rowSF
3rd rowSF
4th rowSF
5th rowSF

Common Values

ValueCountFrequency (%)
SF 378440
76.6%
S0 87007
 
17.6%
REJ 26875
 
5.4%
RSTR 903
 
0.2%
RSTO 579
 
0.1%
SH 107
 
< 0.1%
S1 57
 
< 0.1%
S2 24
 
< 0.1%
RSTOS0 11
 
< 0.1%
S3 10
 
< 0.1%

Length

2024-10-22T19:34:24.442777image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sf 378440
76.6%
s0 87007
 
17.6%
rej 26875
 
5.4%
rstr 903
 
0.2%
rsto 579
 
0.1%
sh 107
 
< 0.1%
s1 57
 
< 0.1%
s2 24
 
< 0.1%
rstos0 11
 
< 0.1%
s3 10
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
S 467149
45.9%
F 378440
37.2%
0 87018
 
8.5%
R 29271
 
2.9%
E 26875
 
2.6%
J 26875
 
2.6%
T 1501
 
0.1%
O 598
 
0.1%
H 115
 
< 0.1%
1 57
 
< 0.1%
Other values (2) 34
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1017933
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 467149
45.9%
F 378440
37.2%
0 87018
 
8.5%
R 29271
 
2.9%
E 26875
 
2.6%
J 26875
 
2.6%
T 1501
 
0.1%
O 598
 
0.1%
H 115
 
< 0.1%
1 57
 
< 0.1%
Other values (2) 34
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1017933
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 467149
45.9%
F 378440
37.2%
0 87018
 
8.5%
R 29271
 
2.9%
E 26875
 
2.6%
J 26875
 
2.6%
T 1501
 
0.1%
O 598
 
0.1%
H 115
 
< 0.1%
1 57
 
< 0.1%
Other values (2) 34
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1017933
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 467149
45.9%
F 378440
37.2%
0 87018
 
8.5%
R 29271
 
2.9%
E 26875
 
2.6%
J 26875
 
2.6%
T 1501
 
0.1%
O 598
 
0.1%
H 115
 
< 0.1%
1 57
 
< 0.1%
Other values (2) 34
 
< 0.1%

src_bytes
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct3300
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3025.6103
Minimum0
Maximum6.9337564 × 108
Zeros115342
Zeros (%)23.3%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2024-10-22T19:34:24.651432image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q145
median520
Q31032
95-th percentile1032
Maximum6.9337564 × 108
Range6.9337564 × 108
Interquartile range (IQR)987

Descriptive statistics

Standard deviation988218.1
Coefficient of variation (CV)326.61777
Kurtosis490584.35
Mean3025.6103
Median Absolute Deviation (MAD)512
Skewness699.21315
Sum1.494715 × 109
Variance9.7657502 × 1011
MonotonicityNot monotonic
2024-10-22T19:34:24.890085image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1032 228035
46.2%
0 115342
23.3%
520 52774
 
10.7%
105 7370
 
1.5%
147 2725
 
0.6%
54540 2143
 
0.4%
146 2033
 
0.4%
42 1069
 
0.2%
8 1045
 
0.2%
28 984
 
0.2%
Other values (3290) 80501
 
16.3%
ValueCountFrequency (%)
0 115342
23.3%
1 257
 
0.1%
4 4
 
< 0.1%
5 12
 
< 0.1%
6 67
 
< 0.1%
7 104
 
< 0.1%
8 1045
 
0.2%
9 155
 
< 0.1%
10 174
 
< 0.1%
11 19
 
< 0.1%
ValueCountFrequency (%)
693375640 1
 
< 0.1%
5135678 21
< 0.1%
5133877 1
 
< 0.1%
5133876 30
< 0.1%
5131424 7
 
< 0.1%
2500058 1
 
< 0.1%
2194619 22
< 0.1%
2104380 1
 
< 0.1%
715240 1
 
< 0.1%
501760 6
 
< 0.1%

dst_bytes
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct10725
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean868.53242
Minimum0
Maximum5155468
Zeros408258
Zeros (%)82.6%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2024-10-22T19:34:25.122484image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2417
Maximum5155468
Range5155468
Interquartile range (IQR)0

Descriptive statistics

Standard deviation33040.001
Coefficient of variation (CV)38.041183
Kurtosis20338.143
Mean868.53242
Median Absolute Deviation (MAD)0
Skewness136.75928
Sum4.2907326 × 108
Variance1.0916417 × 109
MonotonicityNot monotonic
2024-10-22T19:34:25.359561image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 408258
82.6%
105 4451
 
0.9%
147 2501
 
0.5%
146 2289
 
0.5%
8314 2133
 
0.4%
145 985
 
0.2%
42 921
 
0.2%
330 854
 
0.2%
329 804
 
0.2%
331 793
 
0.2%
Other values (10715) 70032
 
14.2%
ValueCountFrequency (%)
0 408258
82.6%
1 5
 
< 0.1%
4 107
 
< 0.1%
5 2
 
< 0.1%
6 1
 
< 0.1%
14 1
 
< 0.1%
15 5
 
< 0.1%
17 29
 
< 0.1%
18 3
 
< 0.1%
20 2
 
< 0.1%
ValueCountFrequency (%)
5155468 1
< 0.1%
5153771 1
< 0.1%
5153460 1
< 0.1%
5151385 1
< 0.1%
5151154 1
< 0.1%
5151049 1
< 0.1%
5150938 1
< 0.1%
5150877 1
< 0.1%
5150841 1
< 0.1%
5150836 1
< 0.1%

land
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size27.3 MiB
0
493999 
1
 
22

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters494021
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 493999
> 99.9%
1 22
 
< 0.1%

Length

2024-10-22T19:34:25.566541image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-22T19:34:25.708964image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 493999
> 99.9%
1 22
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 493999
> 99.9%
1 22
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 494021
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 493999
> 99.9%
1 22
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 494021
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 493999
> 99.9%
1 22
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 494021
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 493999
> 99.9%
1 22
 
< 0.1%

wrong_fragment
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size27.3 MiB
0
492783 
3
 
970
1
 
268

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters494021
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 492783
99.7%
3 970
 
0.2%
1 268
 
0.1%

Length

2024-10-22T19:34:25.858955image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-22T19:34:26.012976image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 492783
99.7%
3 970
 
0.2%
1 268
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 492783
99.7%
3 970
 
0.2%
1 268
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 494021
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 492783
99.7%
3 970
 
0.2%
1 268
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 494021
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 492783
99.7%
3 970
 
0.2%
1 268
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 494021
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 492783
99.7%
3 970
 
0.2%
1 268
 
0.1%

urgent
Categorical

Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size27.3 MiB
0
494017 
1
 
2
2
 
1
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters494021
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 494017
> 99.9%
1 2
 
< 0.1%
2 1
 
< 0.1%
3 1
 
< 0.1%

Length

2024-10-22T19:34:26.172110image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-22T19:34:26.326537image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 494017
> 99.9%
1 2
 
< 0.1%
2 1
 
< 0.1%
3 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 494017
> 99.9%
1 2
 
< 0.1%
2 1
 
< 0.1%
3 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 494021
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 494017
> 99.9%
1 2
 
< 0.1%
2 1
 
< 0.1%
3 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 494021
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 494017
> 99.9%
1 2
 
< 0.1%
2 1
 
< 0.1%
3 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 494021
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 494017
> 99.9%
1 2
 
< 0.1%
2 1
 
< 0.1%
3 1
 
< 0.1%

hot
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct22
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.034518776
Minimum0
Maximum30
Zeros490829
Zeros (%)99.4%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2024-10-22T19:34:26.481826image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum30
Range30
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.78210258
Coefficient of variation (CV)22.65731
Kurtosis1127.0172
Mean0.034518776
Median Absolute Deviation (MAD)0
Skewness32.629145
Sum17053
Variance0.61168445
MonotonicityNot monotonic
2024-10-22T19:34:26.672578image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0 490829
99.4%
2 2192
 
0.4%
28 274
 
0.1%
1 256
 
0.1%
4 112
 
< 0.1%
6 104
 
< 0.1%
5 51
 
< 0.1%
3 38
 
< 0.1%
14 37
 
< 0.1%
22 28
 
< 0.1%
Other values (12) 100
 
< 0.1%
ValueCountFrequency (%)
0 490829
99.4%
1 256
 
0.1%
2 2192
 
0.4%
3 38
 
< 0.1%
4 112
 
< 0.1%
5 51
 
< 0.1%
6 104
 
< 0.1%
7 5
 
< 0.1%
9 1
 
< 0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
30 28
 
< 0.1%
28 274
0.1%
24 13
 
< 0.1%
22 28
 
< 0.1%
20 10
 
< 0.1%
19 23
 
< 0.1%
18 13
 
< 0.1%
17 2
 
< 0.1%
16 1
 
< 0.1%
15 1
 
< 0.1%

num_failed_logins
Real number (ℝ)

Skewed  Zeros 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.00015181541
Minimum0
Maximum5
Zeros493958
Zeros (%)> 99.9%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2024-10-22T19:34:26.862339image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.015519597
Coefficient of variation (CV)102.22676
Kurtosis37221.597
Mean0.00015181541
Median Absolute Deviation (MAD)0
Skewness160.80262
Sum75
Variance0.00024085789
MonotonicityNot monotonic
2024-10-22T19:34:27.042976image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 493958
> 99.9%
1 57
 
< 0.1%
2 3
 
< 0.1%
5 1
 
< 0.1%
4 1
 
< 0.1%
3 1
 
< 0.1%
ValueCountFrequency (%)
0 493958
> 99.9%
1 57
 
< 0.1%
2 3
 
< 0.1%
3 1
 
< 0.1%
4 1
 
< 0.1%
5 1
 
< 0.1%
ValueCountFrequency (%)
5 1
 
< 0.1%
4 1
 
< 0.1%
3 1
 
< 0.1%
2 3
 
< 0.1%
1 57
 
< 0.1%
0 493958
> 99.9%

logged_in
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size27.3 MiB
0
420784 
1
73237 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters494021
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 420784
85.2%
1 73237
 
14.8%

Length

2024-10-22T19:34:27.231718image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-22T19:34:27.369401image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 420784
85.2%
1 73237
 
14.8%

Most occurring characters

ValueCountFrequency (%)
0 420784
85.2%
1 73237
 
14.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 494021
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 420784
85.2%
1 73237
 
14.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 494021
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 420784
85.2%
1 73237
 
14.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 494021
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 420784
85.2%
1 73237
 
14.8%

num_compromised
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct23
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.010212116
Minimum0
Maximum884
Zeros491797
Zeros (%)99.5%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2024-10-22T19:34:27.507289image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum884
Range884
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.7983263
Coefficient of variation (CV)176.09731
Kurtosis188121.35
Mean0.010212116
Median Absolute Deviation (MAD)0
Skewness417.53023
Sum5045
Variance3.2339773
MonotonicityNot monotonic
2024-10-22T19:34:27.682427image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 491797
99.5%
1 2151
 
0.4%
2 24
 
< 0.1%
4 16
 
< 0.1%
3 11
 
< 0.1%
6 3
 
< 0.1%
7 2
 
< 0.1%
5 2
 
< 0.1%
16 1
 
< 0.1%
767 1
 
< 0.1%
Other values (13) 13
 
< 0.1%
ValueCountFrequency (%)
0 491797
99.5%
1 2151
 
0.4%
2 24
 
< 0.1%
3 11
 
< 0.1%
4 16
 
< 0.1%
5 2
 
< 0.1%
6 3
 
< 0.1%
7 2
 
< 0.1%
9 1
 
< 0.1%
11 1
 
< 0.1%
ValueCountFrequency (%)
884 1
< 0.1%
767 1
< 0.1%
281 1
< 0.1%
275 1
< 0.1%
238 1
< 0.1%
102 1
< 0.1%
38 1
< 0.1%
22 1
< 0.1%
21 1
< 0.1%
18 1
< 0.1%

root_shell
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size27.3 MiB
0
493966 
1
 
55

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters494021
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 493966
> 99.9%
1 55
 
< 0.1%

Length

2024-10-22T19:34:27.862157image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-22T19:34:28.003620image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 493966
> 99.9%
1 55
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 493966
> 99.9%
1 55
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 494021
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 493966
> 99.9%
1 55
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 494021
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 493966
> 99.9%
1 55
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 494021
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 493966
> 99.9%
1 55
 
< 0.1%

su_attempted
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size27.3 MiB
0
494009 
1
 
6
2
 
6

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters494021
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 494009
> 99.9%
1 6
 
< 0.1%
2 6
 
< 0.1%

Length

2024-10-22T19:34:28.150570image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-22T19:34:28.295102image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 494009
> 99.9%
1 6
 
< 0.1%
2 6
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 494009
> 99.9%
1 6
 
< 0.1%
2 6
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 494021
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 494009
> 99.9%
1 6
 
< 0.1%
2 6
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 494021
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 494009
> 99.9%
1 6
 
< 0.1%
2 6
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 494021
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 494009
> 99.9%
1 6
 
< 0.1%
2 6
 
< 0.1%

num_root
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.011351744
Minimum0
Maximum993
Zeros493436
Zeros (%)99.9%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2024-10-22T19:34:28.411525image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum993
Range993
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.0127183
Coefficient of variation (CV)177.30476
Kurtosis188933.05
Mean0.011351744
Median Absolute Deviation (MAD)0
Skewness417.06584
Sum5608
Variance4.0510351
MonotonicityNot monotonic
2024-10-22T19:34:28.723276image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 493436
99.9%
1 233
 
< 0.1%
9 167
 
< 0.1%
6 126
 
< 0.1%
2 22
 
< 0.1%
5 12
 
< 0.1%
4 10
 
< 0.1%
3 3
 
< 0.1%
16 1
 
< 0.1%
857 1
 
< 0.1%
Other values (10) 10
 
< 0.1%
ValueCountFrequency (%)
0 493436
99.9%
1 233
 
< 0.1%
2 22
 
< 0.1%
3 3
 
< 0.1%
4 10
 
< 0.1%
5 12
 
< 0.1%
6 126
 
< 0.1%
7 1
 
< 0.1%
9 167
 
< 0.1%
12 1
 
< 0.1%
ValueCountFrequency (%)
993 1
< 0.1%
857 1
< 0.1%
306 1
< 0.1%
278 1
< 0.1%
268 1
< 0.1%
119 1
< 0.1%
54 1
< 0.1%
39 1
< 0.1%
16 1
< 0.1%
14 1
< 0.1%

num_file_creations
Real number (ℝ)

Skewed  Zeros 

Distinct18
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0010829499
Minimum0
Maximum28
Zeros493756
Zeros (%)99.9%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2024-10-22T19:34:28.868549image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum28
Range28
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.096415879
Coefficient of variation (CV)89.030783
Kurtosis43583.895
Mean0.0010829499
Median Absolute Deviation (MAD)0
Skewness192.33477
Sum535
Variance0.0092960217
MonotonicityNot monotonic
2024-10-22T19:34:29.050838image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0 493756
99.9%
1 207
 
< 0.1%
2 36
 
< 0.1%
4 7
 
< 0.1%
16 2
 
< 0.1%
15 1
 
< 0.1%
9 1
 
< 0.1%
28 1
 
< 0.1%
10 1
 
< 0.1%
21 1
 
< 0.1%
Other values (8) 8
 
< 0.1%
ValueCountFrequency (%)
0 493756
99.9%
1 207
 
< 0.1%
2 36
 
< 0.1%
4 7
 
< 0.1%
5 1
 
< 0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
9 1
 
< 0.1%
10 1
 
< 0.1%
12 1
 
< 0.1%
ValueCountFrequency (%)
28 1
< 0.1%
25 1
< 0.1%
22 1
< 0.1%
21 1
< 0.1%
20 1
< 0.1%
16 2
< 0.1%
15 1
< 0.1%
14 1
< 0.1%
12 1
< 0.1%
10 1
< 0.1%

num_shells
Categorical

Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size27.3 MiB
0
493970 
1
 
48
2
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters494021
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 493970
> 99.9%
1 48
 
< 0.1%
2 3
 
< 0.1%

Length

2024-10-22T19:34:29.236412image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-22T19:34:29.387126image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 493970
> 99.9%
1 48
 
< 0.1%
2 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 493970
> 99.9%
1 48
 
< 0.1%
2 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 494021
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 493970
> 99.9%
1 48
 
< 0.1%
2 3
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 494021
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 493970
> 99.9%
1 48
 
< 0.1%
2 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 494021
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 493970
> 99.9%
1 48
 
< 0.1%
2 3
 
< 0.1%

num_access_files
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0010080543
Minimum0
Maximum8
Zeros493567
Zeros (%)99.9%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2024-10-22T19:34:29.533708image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum8
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.03648169
Coefficient of variation (CV)36.190203
Kurtosis7571.4041
Mean0.0010080543
Median Absolute Deviation (MAD)0
Skewness61.201452
Sum498
Variance0.0013309137
MonotonicityNot monotonic
2024-10-22T19:34:29.695642image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 493567
99.9%
1 424
 
0.1%
2 25
 
< 0.1%
3 2
 
< 0.1%
4 1
 
< 0.1%
6 1
 
< 0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
0 493567
99.9%
1 424
 
0.1%
2 25
 
< 0.1%
3 2
 
< 0.1%
4 1
 
< 0.1%
6 1
 
< 0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
8 1
 
< 0.1%
6 1
 
< 0.1%
4 1
 
< 0.1%
3 2
 
< 0.1%
2 25
 
< 0.1%
1 424
 
0.1%
0 493567
99.9%

num_outbound_cmds
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size27.3 MiB
0
494021 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters494021
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 494021
100.0%

Length

2024-10-22T19:34:29.887552image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-22T19:34:30.046874image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 494021
100.0%

Most occurring characters

ValueCountFrequency (%)
0 494021
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 494021
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 494021
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 494021
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 494021
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 494021
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 494021
100.0%

is_host_login
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size27.3 MiB
0
494021 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters494021
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 494021
100.0%

Length

2024-10-22T19:34:30.206150image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-22T19:34:30.353615image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 494021
100.0%

Most occurring characters

ValueCountFrequency (%)
0 494021
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 494021
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 494021
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 494021
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 494021
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 494021
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 494021
100.0%

is_guest_login
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size27.3 MiB
0
493336 
1
 
685

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters494021
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 493336
99.9%
1 685
 
0.1%

Length

2024-10-22T19:34:30.497687image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-22T19:34:30.625520image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 493336
99.9%
1 685
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 493336
99.9%
1 685
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 494021
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 493336
99.9%
1 685
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 494021
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 493336
99.9%
1 685
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 494021
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 493336
99.9%
1 685
 
0.1%

count
Real number (ℝ)

High correlation 

Distinct490
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean332.28569
Minimum0
Maximum511
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2024-10-22T19:34:30.782160image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q1117
median510
Q3511
95-th percentile511
Maximum511
Range511
Interquartile range (IQR)394

Descriptive statistics

Standard deviation213.14741
Coefficient of variation (CV)0.64145829
Kurtosis-1.4722098
Mean332.28569
Median Absolute Deviation (MAD)1
Skewness-0.54200562
Sum1.6415611 × 108
Variance45431.819
MonotonicityNot monotonic
2024-10-22T19:34:31.006679image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
511 227895
46.1%
1 39214
 
7.9%
510 26598
 
5.4%
2 11219
 
2.3%
3 5812
 
1.2%
509 5605
 
1.1%
4 5400
 
1.1%
5 4469
 
0.9%
6 3708
 
0.8%
7 3254
 
0.7%
Other values (480) 160847
32.6%
ValueCountFrequency (%)
0 2
 
< 0.1%
1 39214
7.9%
2 11219
 
2.3%
3 5812
 
1.2%
4 5400
 
1.1%
5 4469
 
0.9%
6 3708
 
0.8%
7 3254
 
0.7%
8 2921
 
0.6%
9 2692
 
0.5%
ValueCountFrequency (%)
511 227895
46.1%
510 26598
 
5.4%
509 5605
 
1.1%
508 1426
 
0.3%
507 541
 
0.1%
506 163
 
< 0.1%
505 79
 
< 0.1%
504 161
 
< 0.1%
503 40
 
< 0.1%
502 42
 
< 0.1%

srv_count
Real number (ℝ)

High correlation 

Distinct470
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean292.90656
Minimum0
Maximum511
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2024-10-22T19:34:31.230683image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q110
median510
Q3511
95-th percentile511
Maximum511
Range511
Interquartile range (IQR)501

Descriptive statistics

Standard deviation246.32282
Coefficient of variation (CV)0.84096041
Kurtosis-1.9151417
Mean292.90656
Median Absolute Deviation (MAD)1
Skewness-0.27384793
Sum1.4470199 × 108
Variance60674.93
MonotonicityNot monotonic
2024-10-22T19:34:31.453350image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
511 226559
45.9%
1 37001
 
7.5%
510 26898
 
5.4%
2 18857
 
3.8%
3 11280
 
2.3%
4 9875
 
2.0%
5 9160
 
1.9%
6 8615
 
1.7%
8 8150
 
1.6%
7 7982
 
1.6%
Other values (460) 129644
26.2%
ValueCountFrequency (%)
0 2
 
< 0.1%
1 37001
7.5%
2 18857
3.8%
3 11280
 
2.3%
4 9875
 
2.0%
5 9160
 
1.9%
6 8615
 
1.7%
7 7982
 
1.6%
8 8150
 
1.6%
9 7557
 
1.5%
ValueCountFrequency (%)
511 226559
45.9%
510 26898
 
5.4%
509 5433
 
1.1%
508 1377
 
0.3%
507 541
 
0.1%
506 163
 
< 0.1%
505 79
 
< 0.1%
504 161
 
< 0.1%
503 40
 
< 0.1%
502 42
 
< 0.1%

serror_rate
Real number (ℝ)

High correlation  Zeros 

Distinct92
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.17668666
Minimum0
Maximum1
Zeros404787
Zeros (%)81.9%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2024-10-22T19:34:31.680357image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.38071696
Coefficient of variation (CV)2.1547578
Kurtosis0.8855008
Mean0.17668666
Median Absolute Deviation (MAD)0
Skewness1.6975981
Sum87286.92
Variance0.1449454
MonotonicityNot monotonic
2024-10-22T19:34:31.905367image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 404787
81.9%
1 86537
 
17.5%
0.99 311
 
0.1%
0.08 155
 
< 0.1%
0.05 150
 
< 0.1%
0.06 129
 
< 0.1%
0.07 129
 
< 0.1%
0.14 118
 
< 0.1%
0.04 115
 
< 0.1%
0.01 109
 
< 0.1%
Other values (82) 1481
 
0.3%
ValueCountFrequency (%)
0 404787
81.9%
0.01 109
 
< 0.1%
0.02 62
 
< 0.1%
0.03 92
 
< 0.1%
0.04 115
 
< 0.1%
0.05 150
 
< 0.1%
0.06 129
 
< 0.1%
0.07 129
 
< 0.1%
0.08 155
 
< 0.1%
0.09 102
 
< 0.1%
ValueCountFrequency (%)
1 86537
17.5%
0.99 311
 
0.1%
0.98 18
 
< 0.1%
0.97 10
 
< 0.1%
0.96 8
 
< 0.1%
0.95 6
 
< 0.1%
0.94 6
 
< 0.1%
0.93 4
 
< 0.1%
0.92 2
 
< 0.1%
0.91 2
 
< 0.1%

srv_serror_rate
Real number (ℝ)

High correlation  Zeros 

Distinct51
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.17660881
Minimum0
Maximum1
Zeros405686
Zeros (%)82.1%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2024-10-22T19:34:32.138654image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.38101658
Coefficient of variation (CV)2.1574042
Kurtosis0.88220351
Mean0.17660881
Median Absolute Deviation (MAD)0
Skewness1.6972073
Sum87248.46
Variance0.14517364
MonotonicityNot monotonic
2024-10-22T19:34:32.363263image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 405686
82.1%
1 87052
 
17.6%
0.03 139
 
< 0.1%
0.04 120
 
< 0.1%
0.05 109
 
< 0.1%
0.06 98
 
< 0.1%
0.02 84
 
< 0.1%
0.5 78
 
< 0.1%
0.08 73
 
< 0.1%
0.07 68
 
< 0.1%
Other values (41) 514
 
0.1%
ValueCountFrequency (%)
0 405686
82.1%
0.01 10
 
< 0.1%
0.02 84
 
< 0.1%
0.03 139
 
< 0.1%
0.04 120
 
< 0.1%
0.05 109
 
< 0.1%
0.06 98
 
< 0.1%
0.07 68
 
< 0.1%
0.08 73
 
< 0.1%
0.09 48
 
< 0.1%
ValueCountFrequency (%)
1 87052
17.6%
0.95 5
 
< 0.1%
0.94 5
 
< 0.1%
0.93 2
 
< 0.1%
0.92 6
 
< 0.1%
0.91 3
 
< 0.1%
0.9 1
 
< 0.1%
0.88 4
 
< 0.1%
0.86 1
 
< 0.1%
0.85 2
 
< 0.1%

rerror_rate
Real number (ℝ)

High correlation  Zeros 

Distinct77
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.057433409
Minimum0
Maximum1
Zeros464948
Zeros (%)94.1%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2024-10-22T19:34:32.584799image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.23162347
Coefficient of variation (CV)4.0329049
Kurtosis12.457472
Mean0.057433409
Median Absolute Deviation (MAD)0
Skewness3.798979
Sum28373.31
Variance0.053649434
MonotonicityNot monotonic
2024-10-22T19:34:32.813907image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 464948
94.1%
1 26979
 
5.5%
0.86 113
 
< 0.1%
0.87 102
 
< 0.1%
0.92 97
 
< 0.1%
0.25 92
 
< 0.1%
0.9 91
 
< 0.1%
0.95 91
 
< 0.1%
0.5 78
 
< 0.1%
0.91 75
 
< 0.1%
Other values (67) 1355
 
0.3%
ValueCountFrequency (%)
0 464948
94.1%
0.01 62
 
< 0.1%
0.02 35
 
< 0.1%
0.03 28
 
< 0.1%
0.04 13
 
< 0.1%
0.05 20
 
< 0.1%
0.06 18
 
< 0.1%
0.07 11
 
< 0.1%
0.08 14
 
< 0.1%
0.09 12
 
< 0.1%
ValueCountFrequency (%)
1 26979
5.5%
0.99 49
 
< 0.1%
0.98 34
 
< 0.1%
0.97 36
 
< 0.1%
0.96 66
 
< 0.1%
0.95 91
 
< 0.1%
0.94 63
 
< 0.1%
0.93 64
 
< 0.1%
0.92 97
 
< 0.1%
0.91 75
 
< 0.1%

srv_rerror_rate
Real number (ℝ)

High correlation  Zeros 

Distinct51
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.057718943
Minimum0
Maximum1
Zeros464320
Zeros (%)94.0%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2024-10-22T19:34:33.280197image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.23214698
Coefficient of variation (CV)4.0220241
Kurtosis12.472139
Mean0.057718943
Median Absolute Deviation (MAD)0
Skewness3.7999865
Sum28514.37
Variance0.053892221
MonotonicityNot monotonic
2024-10-22T19:34:33.517797image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 464320
94.0%
1 28116
 
5.7%
0.33 252
 
0.1%
0.5 201
 
< 0.1%
0.25 173
 
< 0.1%
0.2 143
 
< 0.1%
0.17 128
 
< 0.1%
0.14 73
 
< 0.1%
0.04 63
 
< 0.1%
0.03 61
 
< 0.1%
Other values (41) 491
 
0.1%
ValueCountFrequency (%)
0 464320
94.0%
0.01 6
 
< 0.1%
0.02 54
 
< 0.1%
0.03 61
 
< 0.1%
0.04 63
 
< 0.1%
0.05 41
 
< 0.1%
0.06 54
 
< 0.1%
0.07 36
 
< 0.1%
0.08 28
 
< 0.1%
0.09 24
 
< 0.1%
ValueCountFrequency (%)
1 28116
5.7%
0.96 2
 
< 0.1%
0.95 1
 
< 0.1%
0.94 1
 
< 0.1%
0.93 1
 
< 0.1%
0.92 2
 
< 0.1%
0.88 2
 
< 0.1%
0.87 1
 
< 0.1%
0.86 3
 
< 0.1%
0.85 1
 
< 0.1%

same_srv_rate
Real number (ℝ)

High correlation 

Distinct99
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.79154734
Minimum0
Maximum1
Zeros3627
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2024-10-22T19:34:33.745647image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.03
Q11
median1
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.38818949
Coefficient of variation (CV)0.49041854
Kurtosis-0.17102919
Mean0.79154734
Median Absolute Deviation (MAD)0
Skewness-1.3421414
Sum391041.01
Variance0.15069108
MonotonicityNot monotonic
2024-10-22T19:34:33.982913image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 382079
77.3%
0.06 11196
 
2.3%
0.05 10582
 
2.1%
0.04 10161
 
2.1%
0.07 10028
 
2.0%
0.03 9554
 
1.9%
0.02 9377
 
1.9%
0.01 8746
 
1.8%
0.08 7298
 
1.5%
0.09 4974
 
1.0%
Other values (89) 30026
 
6.1%
ValueCountFrequency (%)
0 3627
 
0.7%
0.01 8746
1.8%
0.02 9377
1.9%
0.03 9554
1.9%
0.04 10161
2.1%
0.05 10582
2.1%
0.06 11196
2.3%
0.07 10028
2.0%
0.08 7298
1.5%
0.09 4974
1.0%
ValueCountFrequency (%)
1 382079
77.3%
0.99 144
 
< 0.1%
0.98 94
 
< 0.1%
0.97 35
 
< 0.1%
0.96 10
 
< 0.1%
0.95 12
 
< 0.1%
0.94 12
 
< 0.1%
0.93 6
 
< 0.1%
0.92 15
 
< 0.1%
0.91 7
 
< 0.1%

diff_srv_rate
Real number (ℝ)

High correlation  Zeros 

Distinct78
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.020982387
Minimum0
Maximum1
Zeros382021
Zeros (%)77.3%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2024-10-22T19:34:34.210396image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.07
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.082205493
Coefficient of variation (CV)3.9178332
Kurtosis105.1129
Mean0.020982387
Median Absolute Deviation (MAD)0
Skewness9.6424263
Sum10365.74
Variance0.0067577431
MonotonicityNot monotonic
2024-10-22T19:34:34.433809image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 382021
77.3%
0.06 52812
 
10.7%
0.07 28798
 
5.8%
0.05 19218
 
3.9%
0.08 3254
 
0.7%
1 2358
 
0.5%
0.04 955
 
0.2%
0.67 664
 
0.1%
0.5 590
 
0.1%
0.09 402
 
0.1%
Other values (68) 2949
 
0.6%
ValueCountFrequency (%)
0 382021
77.3%
0.01 105
 
< 0.1%
0.02 60
 
< 0.1%
0.03 98
 
< 0.1%
0.04 955
 
0.2%
0.05 19218
 
3.9%
0.06 52812
 
10.7%
0.07 28798
 
5.8%
0.08 3254
 
0.7%
0.09 402
 
0.1%
ValueCountFrequency (%)
1 2358
0.5%
0.99 8
 
< 0.1%
0.97 2
 
< 0.1%
0.96 32
 
< 0.1%
0.95 17
 
< 0.1%
0.89 3
 
< 0.1%
0.88 1
 
< 0.1%
0.86 1
 
< 0.1%
0.83 1
 
< 0.1%
0.82 1
 
< 0.1%

srv_diff_host_rate
Real number (ℝ)

High correlation  Zeros 

Distinct64
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.028996804
Minimum0
Maximum1
Zeros459377
Zeros (%)93.0%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2024-10-22T19:34:34.650285image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.14
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.14239747
Coefficient of variation (CV)4.9107987
Kurtosis35.114936
Mean0.028996804
Median Absolute Deviation (MAD)0
Skewness5.8690112
Sum14325.03
Variance0.020277039
MonotonicityNot monotonic
2024-10-22T19:34:34.882468image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 459377
93.0%
1 8099
 
1.6%
0.12 1508
 
0.3%
0.5 1418
 
0.3%
0.67 1410
 
0.3%
0.33 1243
 
0.3%
0.11 1207
 
0.2%
0.25 1120
 
0.2%
0.1 1066
 
0.2%
0.14 1045
 
0.2%
Other values (54) 16528
 
3.3%
ValueCountFrequency (%)
0 459377
93.0%
0.01 416
 
0.1%
0.02 496
 
0.1%
0.03 167
 
< 0.1%
0.04 296
 
0.1%
0.05 494
 
0.1%
0.06 705
 
0.1%
0.07 858
 
0.2%
0.08 990
 
0.2%
0.09 930
 
0.2%
ValueCountFrequency (%)
1 8099
1.6%
0.88 2
 
< 0.1%
0.86 3
 
< 0.1%
0.83 16
 
< 0.1%
0.8 75
 
< 0.1%
0.77 1
 
< 0.1%
0.75 359
 
0.1%
0.71 12
 
< 0.1%
0.7 1
 
< 0.1%
0.67 1410
 
0.3%

dst_host_count
Real number (ℝ)

High correlation 

Distinct256
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean232.47078
Minimum0
Maximum255
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2024-10-22T19:34:35.092362image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile33
Q1255
median255
Q3255
95-th percentile255
Maximum255
Range255
Interquartile range (IQR)0

Descriptive statistics

Standard deviation64.74538
Coefficient of variation (CV)0.27850976
Kurtosis5.852867
Mean232.47078
Median Absolute Deviation (MAD)0
Skewness-2.7306878
Sum1.1484545 × 108
Variance4191.9643
MonotonicityNot monotonic
2024-10-22T19:34:35.313322image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
255 432829
87.6%
1 2884
 
0.6%
2 2023
 
0.4%
3 1434
 
0.3%
4 1317
 
0.3%
5 1073
 
0.2%
6 1007
 
0.2%
7 866
 
0.2%
8 842
 
0.2%
9 814
 
0.2%
Other values (246) 48932
 
9.9%
ValueCountFrequency (%)
0 3
 
< 0.1%
1 2884
0.6%
2 2023
0.4%
3 1434
0.3%
4 1317
0.3%
5 1073
 
0.2%
6 1007
 
0.2%
7 866
 
0.2%
8 842
 
0.2%
9 814
 
0.2%
ValueCountFrequency (%)
255 432829
87.6%
254 70
 
< 0.1%
253 71
 
< 0.1%
252 78
 
< 0.1%
251 80
 
< 0.1%
250 83
 
< 0.1%
249 71
 
< 0.1%
248 88
 
< 0.1%
247 78
 
< 0.1%
246 61
 
< 0.1%

dst_host_srv_count
Real number (ℝ)

High correlation 

Distinct256
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean188.66567
Minimum0
Maximum255
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2024-10-22T19:34:35.531152image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q146
median255
Q3255
95-th percentile255
Maximum255
Range255
Interquartile range (IQR)209

Descriptive statistics

Standard deviation106.04044
Coefficient of variation (CV)0.56205476
Kurtosis-0.8741455
Mean188.66567
Median Absolute Deviation (MAD)0
Skewness-1.0348572
Sum93204803
Variance11244.574
MonotonicityNot monotonic
2024-10-22T19:34:35.766807image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
255 337746
68.4%
1 11895
 
2.4%
2 7243
 
1.5%
3 5855
 
1.2%
11 5627
 
1.1%
8 5579
 
1.1%
10 5550
 
1.1%
5 5494
 
1.1%
6 5394
 
1.1%
12 5388
 
1.1%
Other values (246) 98250
 
19.9%
ValueCountFrequency (%)
0 3
 
< 0.1%
1 11895
2.4%
2 7243
1.5%
3 5855
1.2%
4 5382
1.1%
5 5494
1.1%
6 5394
1.1%
7 5265
1.1%
8 5579
1.1%
9 5261
1.1%
ValueCountFrequency (%)
255 337746
68.4%
254 1067
 
0.2%
253 685
 
0.1%
252 513
 
0.1%
251 628
 
0.1%
250 721
 
0.1%
249 600
 
0.1%
248 389
 
0.1%
247 392
 
0.1%
246 408
 
0.1%

dst_host_same_srv_rate
Real number (ℝ)

High correlation  Zeros 

Distinct101
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7537797
Minimum0
Maximum1
Zeros11469
Zeros (%)2.3%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2024-10-22T19:34:35.989456image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.02
Q10.41
median1
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.59

Descriptive statistics

Standard deviation0.41078098
Coefficient of variation (CV)0.54496158
Kurtosis-0.68724913
Mean0.7537797
Median Absolute Deviation (MAD)0
Skewness-1.1255329
Sum372383
Variance0.16874101
MonotonicityNot monotonic
2024-10-22T19:34:36.213853image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 347828
70.4%
0.04 16092
 
3.3%
0.02 15880
 
3.2%
0.05 15414
 
3.1%
0.07 15165
 
3.1%
0.01 12839
 
2.6%
0 11469
 
2.3%
0.03 10656
 
2.2%
0.06 9974
 
2.0%
0.08 6199
 
1.3%
Other values (91) 32505
 
6.6%
ValueCountFrequency (%)
0 11469
2.3%
0.01 12839
2.6%
0.02 15880
3.2%
0.03 10656
2.2%
0.04 16092
3.3%
0.05 15414
3.1%
0.06 9974
2.0%
0.07 15165
3.1%
0.08 6199
 
1.3%
0.09 2905
 
0.6%
ValueCountFrequency (%)
1 347828
70.4%
0.99 1045
 
0.2%
0.98 1753
 
0.4%
0.97 822
 
0.2%
0.96 1473
 
0.3%
0.95 1372
 
0.3%
0.94 1109
 
0.2%
0.93 1095
 
0.2%
0.92 891
 
0.2%
0.91 945
 
0.2%

dst_host_diff_srv_rate
Real number (ℝ)

High correlation  Zeros 

Distinct101
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.030905731
Minimum0
Maximum1
Zeros347031
Zeros (%)70.2%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2024-10-22T19:34:36.440608image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.04
95-th percentile0.08
Maximum1
Range1
Interquartile range (IQR)0.04

Descriptive statistics

Standard deviation0.10925911
Coefficient of variation (CV)3.535238
Kurtosis50.290263
Mean0.030905731
Median Absolute Deviation (MAD)0
Skewness6.8571685
Sum15268.08
Variance0.011937554
MonotonicityNot monotonic
2024-10-22T19:34:36.907176image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 347031
70.2%
0.07 45922
 
9.3%
0.06 28224
 
5.7%
0.05 18466
 
3.7%
0.08 14540
 
2.9%
0.01 13341
 
2.7%
0.02 4543
 
0.9%
0.09 3909
 
0.8%
0.03 3519
 
0.7%
0.04 2811
 
0.6%
Other values (91) 11715
 
2.4%
ValueCountFrequency (%)
0 347031
70.2%
0.01 13341
 
2.7%
0.02 4543
 
0.9%
0.03 3519
 
0.7%
0.04 2811
 
0.6%
0.05 18466
 
3.7%
0.06 28224
 
5.7%
0.07 45922
 
9.3%
0.08 14540
 
2.9%
0.09 3909
 
0.8%
ValueCountFrequency (%)
1 2013
0.4%
0.99 6
 
< 0.1%
0.98 12
 
< 0.1%
0.97 11
 
< 0.1%
0.96 11
 
< 0.1%
0.95 14
 
< 0.1%
0.94 10
 
< 0.1%
0.93 8
 
< 0.1%
0.92 7
 
< 0.1%
0.91 61
 
< 0.1%

dst_host_same_src_port_rate
Real number (ℝ)

High correlation  Zeros 

Distinct101
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.60193476
Minimum0
Maximum1
Zeros142860
Zeros (%)28.9%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2024-10-22T19:34:37.143979image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.48130925
Coefficient of variation (CV)0.79960369
Kurtosis-1.8193508
Mean0.60193476
Median Absolute Deviation (MAD)0
Skewness-0.40059615
Sum297368.41
Variance0.2316586
MonotonicityNot monotonic
2024-10-22T19:34:37.360873image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 288883
58.5%
0 142860
28.9%
0.01 21912
 
4.4%
0.02 7228
 
1.5%
0.03 4419
 
0.9%
0.04 2749
 
0.6%
0.05 2176
 
0.4%
0.06 1784
 
0.4%
0.5 1642
 
0.3%
0.08 1428
 
0.3%
Other values (91) 18940
 
3.8%
ValueCountFrequency (%)
0 142860
28.9%
0.01 21912
 
4.4%
0.02 7228
 
1.5%
0.03 4419
 
0.9%
0.04 2749
 
0.6%
0.05 2176
 
0.4%
0.06 1784
 
0.4%
0.07 1412
 
0.3%
0.08 1428
 
0.3%
0.09 844
 
0.2%
ValueCountFrequency (%)
1 288883
58.5%
0.99 613
 
0.1%
0.98 466
 
0.1%
0.97 220
 
< 0.1%
0.96 285
 
0.1%
0.95 249
 
0.1%
0.94 113
 
< 0.1%
0.93 151
 
< 0.1%
0.92 118
 
< 0.1%
0.91 116
 
< 0.1%

dst_host_srv_diff_host_rate
Real number (ℝ)

High correlation  Zeros 

Distinct65
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0066835013
Minimum0
Maximum1
Zeros441889
Zeros (%)89.4%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2024-10-22T19:34:37.617825image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.03
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.042132874
Coefficient of variation (CV)6.3040123
Kurtosis271.01788
Mean0.0066835013
Median Absolute Deviation (MAD)0
Skewness14.35258
Sum3301.79
Variance0.0017751791
MonotonicityNot monotonic
2024-10-22T19:34:37.865285image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 441889
89.4%
0.02 11738
 
2.4%
0.01 10530
 
2.1%
0.04 6673
 
1.4%
0.03 6624
 
1.3%
0.05 4622
 
0.9%
0.06 1949
 
0.4%
0.07 1545
 
0.3%
0.5 763
 
0.2%
0.08 685
 
0.1%
Other values (55) 7003
 
1.4%
ValueCountFrequency (%)
0 441889
89.4%
0.01 10530
 
2.1%
0.02 11738
 
2.4%
0.03 6624
 
1.3%
0.04 6673
 
1.4%
0.05 4622
 
0.9%
0.06 1949
 
0.4%
0.07 1545
 
0.3%
0.08 685
 
0.1%
0.09 630
 
0.1%
ValueCountFrequency (%)
1 354
0.1%
0.8 1
 
< 0.1%
0.75 1
 
< 0.1%
0.7 1
 
< 0.1%
0.67 41
 
< 0.1%
0.62 1
 
< 0.1%
0.6 12
 
< 0.1%
0.58 1
 
< 0.1%
0.57 11
 
< 0.1%
0.56 8
 
< 0.1%

dst_host_serror_rate
Real number (ℝ)

High correlation  Zeros 

Distinct100
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.17675396
Minimum0
Maximum1
Zeros399810
Zeros (%)80.9%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2024-10-22T19:34:38.107573image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3805931
Coefficient of variation (CV)2.1532366
Kurtosis0.88814719
Mean0.17675396
Median Absolute Deviation (MAD)0
Skewness1.6983685
Sum87320.17
Variance0.14485111
MonotonicityNot monotonic
2024-10-22T19:34:38.341600image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 399810
80.9%
1 86759
 
17.6%
0.01 3670
 
0.7%
0.02 989
 
0.2%
0.03 425
 
0.1%
0.09 269
 
0.1%
0.04 200
 
< 0.1%
0.05 183
 
< 0.1%
0.07 131
 
< 0.1%
0.08 129
 
< 0.1%
Other values (90) 1456
 
0.3%
ValueCountFrequency (%)
0 399810
80.9%
0.01 3670
 
0.7%
0.02 989
 
0.2%
0.03 425
 
0.1%
0.04 200
 
< 0.1%
0.05 183
 
< 0.1%
0.06 107
 
< 0.1%
0.07 131
 
< 0.1%
0.08 129
 
< 0.1%
0.09 269
 
0.1%
ValueCountFrequency (%)
1 86759
17.6%
0.99 35
 
< 0.1%
0.98 17
 
< 0.1%
0.97 11
 
< 0.1%
0.96 12
 
< 0.1%
0.95 8
 
< 0.1%
0.94 11
 
< 0.1%
0.93 7
 
< 0.1%
0.92 6
 
< 0.1%
0.91 5
 
< 0.1%

dst_host_srv_serror_rate
Real number (ℝ)

High correlation  Zeros 

Distinct72
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.17644262
Minimum0
Maximum1
Zeros400945
Zeros (%)81.2%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2024-10-22T19:34:38.570258image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.38091945
Coefficient of variation (CV)2.1588857
Kurtosis0.88711562
Mean0.17644262
Median Absolute Deviation (MAD)0
Skewness1.6989593
Sum87166.36
Variance0.14509963
MonotonicityNot monotonic
2024-10-22T19:34:38.794122image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 400945
81.2%
1 86997
 
17.6%
0.01 4868
 
1.0%
0.02 675
 
0.1%
0.03 149
 
< 0.1%
0.04 85
 
< 0.1%
0.05 49
 
< 0.1%
0.06 30
 
< 0.1%
0.08 15
 
< 0.1%
0.07 14
 
< 0.1%
Other values (62) 194
 
< 0.1%
ValueCountFrequency (%)
0 400945
81.2%
0.01 4868
 
1.0%
0.02 675
 
0.1%
0.03 149
 
< 0.1%
0.04 85
 
< 0.1%
0.05 49
 
< 0.1%
0.06 30
 
< 0.1%
0.07 14
 
< 0.1%
0.08 15
 
< 0.1%
0.09 10
 
< 0.1%
ValueCountFrequency (%)
1 86997
17.6%
0.98 5
 
< 0.1%
0.97 5
 
< 0.1%
0.96 7
 
< 0.1%
0.95 3
 
< 0.1%
0.94 2
 
< 0.1%
0.93 3
 
< 0.1%
0.92 2
 
< 0.1%
0.91 3
 
< 0.1%
0.9 1
 
< 0.1%

dst_host_rerror_rate
Real number (ℝ)

High correlation  Zeros 

Distinct101
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.05811761
Minimum0
Maximum1
Zeros458792
Zeros (%)92.9%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2024-10-22T19:34:38.998587image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.23058951
Coefficient of variation (CV)3.9676357
Kurtosis12.38272
Mean0.05811761
Median Absolute Deviation (MAD)0
Skewness3.7812948
Sum28711.32
Variance0.053171521
MonotonicityNot monotonic
2024-10-22T19:34:39.170032image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 458792
92.9%
1 26040
 
5.3%
0.01 1596
 
0.3%
0.02 932
 
0.2%
0.04 801
 
0.2%
0.05 750
 
0.2%
0.03 565
 
0.1%
0.06 238
 
< 0.1%
0.85 131
 
< 0.1%
0.93 116
 
< 0.1%
Other values (91) 4060
 
0.8%
ValueCountFrequency (%)
0 458792
92.9%
0.01 1596
 
0.3%
0.02 932
 
0.2%
0.03 565
 
0.1%
0.04 801
 
0.2%
0.05 750
 
0.2%
0.06 238
 
< 0.1%
0.07 97
 
< 0.1%
0.08 97
 
< 0.1%
0.09 68
 
< 0.1%
ValueCountFrequency (%)
1 26040
5.3%
0.99 66
 
< 0.1%
0.98 64
 
< 0.1%
0.97 67
 
< 0.1%
0.96 81
 
< 0.1%
0.95 115
 
< 0.1%
0.94 63
 
< 0.1%
0.93 116
 
< 0.1%
0.92 103
 
< 0.1%
0.91 80
 
< 0.1%

dst_host_srv_rerror_rate
Real number (ℝ)

High correlation  Zeros 

Distinct101
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.057411669
Minimum0
Maximum1
Zeros459805
Zeros (%)93.1%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2024-10-22T19:34:39.344349image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.23014032
Coefficient of variation (CV)4.0085984
Kurtosis12.54256
Mean0.057411669
Median Absolute Deviation (MAD)0
Skewness3.8057739
Sum28362.57
Variance0.052964569
MonotonicityNot monotonic
2024-10-22T19:34:39.522243image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 459805
93.1%
1 25695
 
5.2%
0.01 1851
 
0.4%
0.04 830
 
0.2%
0.02 783
 
0.2%
0.05 773
 
0.2%
0.03 528
 
0.1%
0.98 322
 
0.1%
0.99 294
 
0.1%
0.06 246
 
< 0.1%
Other values (91) 2894
 
0.6%
ValueCountFrequency (%)
0 459805
93.1%
0.01 1851
 
0.4%
0.02 783
 
0.2%
0.03 528
 
0.1%
0.04 830
 
0.2%
0.05 773
 
0.2%
0.06 246
 
< 0.1%
0.07 94
 
< 0.1%
0.08 43
 
< 0.1%
0.09 31
 
< 0.1%
ValueCountFrequency (%)
1 25695
5.2%
0.99 294
 
0.1%
0.98 322
 
0.1%
0.97 147
 
< 0.1%
0.96 103
 
< 0.1%
0.95 108
 
< 0.1%
0.94 100
 
< 0.1%
0.93 99
 
< 0.1%
0.92 61
 
< 0.1%
0.91 68
 
< 0.1%

outcome
Categorical

High correlation  Imbalance 

Distinct23
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size30.0 MiB
smurf.
280790 
neptune.
107201 
normal.
97278 
back.
 
2203
satan.
 
1589
Other values (18)
 
4960

Length

Max length16
Median length6
Mean length6.6584538
Min length4

Characters and Unicode

Total characters3289416
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownormal.
2nd rownormal.
3rd rownormal.
4th rownormal.
5th rownormal.

Common Values

ValueCountFrequency (%)
smurf. 280790
56.8%
neptune. 107201
 
21.7%
normal. 97278
 
19.7%
back. 2203
 
0.4%
satan. 1589
 
0.3%
ipsweep. 1247
 
0.3%
portsweep. 1040
 
0.2%
warezclient. 1020
 
0.2%
teardrop. 979
 
0.2%
pod. 264
 
0.1%
Other values (13) 410
 
0.1%

Length

2024-10-22T19:34:39.691958image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
smurf 280790
56.8%
neptune 107201
 
21.7%
normal 97278
 
19.7%
back 2203
 
0.4%
satan 1589
 
0.3%
ipsweep 1247
 
0.3%
portsweep 1040
 
0.2%
warezclient 1020
 
0.2%
teardrop 979
 
0.2%
pod 264
 
0.1%
Other values (13) 410
 
0.1%

Most occurring characters

ValueCountFrequency (%)
. 494021
15.0%
u 388090
11.8%
r 382207
11.6%
m 378347
11.5%
n 314541
9.6%
s 284900
8.7%
f 280892
8.5%
e 222168
6.8%
p 113338
 
3.4%
t 111892
 
3.4%
Other values (15) 319020
9.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3289416
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 494021
15.0%
u 388090
11.8%
r 382207
11.6%
m 378347
11.5%
n 314541
9.6%
s 284900
8.7%
f 280892
8.5%
e 222168
6.8%
p 113338
 
3.4%
t 111892
 
3.4%
Other values (15) 319020
9.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3289416
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 494021
15.0%
u 388090
11.8%
r 382207
11.6%
m 378347
11.5%
n 314541
9.6%
s 284900
8.7%
f 280892
8.5%
e 222168
6.8%
p 113338
 
3.4%
t 111892
 
3.4%
Other values (15) 319020
9.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3289416
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 494021
15.0%
u 388090
11.8%
r 382207
11.6%
m 378347
11.5%
n 314541
9.6%
s 284900
8.7%
f 280892
8.5%
e 222168
6.8%
p 113338
 
3.4%
t 111892
 
3.4%
Other values (15) 319020
9.7%

Interactions

2024-10-22T19:34:07.317255image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:31:40.934207image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:31:45.920284image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:31:51.522783image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:31:56.963995image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:01.895732image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:06.710905image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:11.592779image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:17.329750image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:23.450338image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:29.346048image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:35.492029image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:41.735018image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:46.644130image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:52.371361image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:57.330073image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:03.987977image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:09.265691image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:14.312510image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:19.924195image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:26.540815image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:32.221293image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:36.907680image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:42.559182image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:47.577044image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:52.537320image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:57.755073image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:34:02.567694image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:34:07.486888image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:31:41.258677image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:31:46.087327image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:31:51.749425image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:31:57.135339image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:02.072430image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:06.892426image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:11.762016image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:17.552057image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:23.628137image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:29.570105image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:35.726493image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:41.911618image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:46.871723image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:52.575658image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:57.586995image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:04.197808image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:09.435360image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:14.498218image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:20.146753image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:26.775744image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:32.397692image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:37.081319image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:42.790110image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:47.747559image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:52.777527image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:57.934009image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:34:02.757043image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:34:07.656932image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:31:41.434103image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:31:46.263789image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:31:51.983868image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:31:57.312497image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:02.251546image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:07.084800image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:11.938625image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:17.779822image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:23.814003image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:29.803186image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:35.974400image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:42.252291image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:47.123348image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:52.759591image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:58.052640image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:04.401944image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:09.612047image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:14.684793image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:20.380066image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:27.012951image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:32.575915image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:37.244730image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:42.994573image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:47.919536image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:53.012164image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:58.106152image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:34:02.943352image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:34:07.985266image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:31:41.609918image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:31:46.426775image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:31:52.195600image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:31:57.481428image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:02.421433image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:07.248239image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:12.117688image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:17.985887image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:24.039254image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:29.977198image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:36.207371image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:42.420042image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:47.339652image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:52.928791image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:58.304220image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:04.618666image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:09.780988image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:14.865138image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:20.598919image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:27.232497image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:32.745117image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:37.402985image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:43.166375image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:48.081170image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:53.243196image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:58.274813image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:34:03.116286image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:34:08.153355image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:31:41.792933image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:31:46.603927image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:31:52.410242image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:31:57.644676image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:02.600700image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:07.423614image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:12.290943image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:18.201795image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:24.265520image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:30.144043image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:36.436810image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:42.600281image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:47.569147image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:53.098065image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:58.562329image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:04.809972image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:09.956616image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:15.042366image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:20.816161image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:27.456897image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:32.916795image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:37.555846image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:43.348578image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:48.245202image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:53.478869image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:58.447865image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:34:03.296038image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:34:08.317839image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:31:41.959252image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:31:46.769593image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:31:52.613572image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:31:57.811565image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:02.765544image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:07.597722image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:12.459820image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:18.423239image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:24.487319image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:30.311790image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:36.674988image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:42.784217image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:47.789849image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:53.262530image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:58.810520image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:05.014047image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:10.129272image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:15.217187image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:21.029925image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:27.668732image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:33.087939image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:37.707972image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:43.517797image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:48.406514image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:53.704349image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:58.615142image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:34:03.473345image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:34:08.482413image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:31:42.116111image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:31:46.941377image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:31:52.812087image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:31:57.973373image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:02.932744image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:07.747026image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:12.622902image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:18.635910image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:24.705036image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:30.481493image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:36.906065image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:42.954916image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:48.012258image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:53.431491image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:59.055639image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:05.220747image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:10.290009image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:15.392876image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:21.235619image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:27.878501image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:33.256993image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:37.856793image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:43.690111image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:48.565240image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:53.926465image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:58.774560image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:34:03.638735image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:34:08.654082image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:31:42.280091image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:31:47.109548image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:31:53.019590image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:31:58.137739image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:03.101903image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:07.912679image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:12.781744image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:18.847168image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:24.933558image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:30.650834image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:37.150467image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:43.128215image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:48.235038image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:53.598991image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:59.308258image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:05.434821image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:10.455853image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:15.567406image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:21.451530image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:28.089276image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:33.425980image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:38.018685image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:43.863189image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:48.730882image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:54.333711image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:58.938033image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:34:03.803197image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:34:08.823904image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:31:42.441779image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:31:47.289921image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:31:53.235915image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:31:58.310217image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:03.268400image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:08.073979image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:12.955144image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:19.054207image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:25.149021image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:30.820384image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:37.391320image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:43.300926image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:48.460971image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:53.768018image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:59.551297image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:05.601697image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:10.624285image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:15.744738image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:21.674288image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:28.306363image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:33.592354image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:38.176127image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:44.039813image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:48.898793image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:54.502645image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:59.102962image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:34:03.976172image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:34:09.002696image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:31:42.616331image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:31:47.606334image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:31:53.483298image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:31:58.492962image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:03.443845image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:08.415843image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:13.136962image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:19.287195image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:25.586196image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:30.997705image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:37.652695image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:43.485823image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:48.702932image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:53.945105image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:59.812233image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:05.782667image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:10.808735image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:15.937714image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:21.908841image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:28.540787image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:33.766748image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:38.341199image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:44.223640image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:49.082619image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:54.684239image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:59.285172image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:34:04.155703image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:34:09.168000image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:31:42.777101image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:31:47.772000image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:31:53.706009image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:31:58.662014image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:03.602864image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:08.573967image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:13.303157image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:19.507535image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:25.807750image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:31.153252image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:37.883188image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:43.653405image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:48.921525image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:54.109988image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:00.062686image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:05.959168image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:10.984288image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:16.110565image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:22.129831image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:28.764196image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:33.954185image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:38.702070image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:44.395349image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:49.251729image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:54.850397image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:59.454032image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:34:04.326951image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:34:09.327773image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:31:42.936594image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:31:47.940214image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:31:53.925512image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:31:58.825620image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:03.767140image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:08.751478image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:13.469489image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:19.724428image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:26.031801image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:31.344901image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:38.099172image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:43.822780image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:49.084907image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:54.273568image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:00.308500image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:06.128436image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:11.150237image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:16.287112image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:22.341197image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:28.974429image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:34.119812image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:38.933724image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:44.568394image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:49.416400image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:55.016356image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:59.621675image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:34:04.488008image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:34:09.490834image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:31:43.113357image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:31:48.109902image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:31:54.091804image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:31:58.993108image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:03.929052image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:08.918551image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:13.640504image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:19.938319image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:26.245432image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:31.579495image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:38.315015image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:43.992330image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:49.253102image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:54.431533image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:00.542679image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:06.300786image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:11.318369image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:16.454015image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:22.759558image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:29.189959image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:34.281760image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:39.147198image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:44.739372image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:49.582321image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:55.177375image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:59.784925image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:34:04.651881image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:34:09.655357image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:31:43.287837image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:31:48.281303image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:31:54.258693image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:31:59.161625image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:04.095538image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:09.083773image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:13.863871image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:20.162388image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:26.463200image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:31.807858image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:38.531637image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:44.160171image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:49.421332image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:54.601812image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:00.782560image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:06.471841image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:11.482106image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:16.651916image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:22.985481image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:29.427791image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:34.447012image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:39.348710image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:44.906073image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:49.742996image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:55.341916image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:59.949100image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:34:04.818312image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:34:09.814788image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:31:43.465141image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:31:48.455370image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:31:54.417165image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:31:59.326524image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:04.264868image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:09.253523image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:14.089351image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:20.379072image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:26.667453image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:32.034799image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:38.764905image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:44.325564image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:49.591719image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:54.744295image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:01.018654image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:06.813213image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:11.649787image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:16.872206image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:23.193835image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:29.652905image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:34.605915image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:39.559133image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:45.074170image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:49.900185image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:55.503012image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:34:00.117220image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:34:04.984351image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:34:09.987037image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:31:43.644273image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:31:48.631344image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:31:54.752398image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:31:59.490509image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:04.440917image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:09.412141image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:14.312178image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:20.599720image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:26.839995image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:32.273925image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:38.997799image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:44.493204image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:49.756161image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:54.911433image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:01.251921image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:06.985555image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:11.826953image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:17.093157image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:23.405468image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:29.870937image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:34.775820image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:39.779760image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:45.241354image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:50.055945image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:55.667472image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:34:00.281684image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:34:05.145426image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:34:10.162466image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:31:43.828288image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:31:48.807559image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:31:54.928222image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:31:59.658256image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:04.615644image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:09.584230image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:14.531273image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:20.815242image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:27.007526image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:32.501301image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:39.229991image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:44.662045image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:49.925459image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:55.074758image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:01.452008image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:07.158942image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:11.992059image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:17.315106image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:23.646166image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:30.078937image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:34.944390image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:39.999593image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:45.410980image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:50.213273image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:55.829668image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:34:00.449313image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:34:05.313590image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:34:10.330609image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:31:44.013879image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:31:49.001281image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:31:55.103212image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:31:59.823652image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:04.785884image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:09.748895image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:14.755411image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:21.032134image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:27.178351image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:32.946861image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:39.470575image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:44.826148image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:50.249011image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:55.239188image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:01.660973image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:07.331948image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:12.151005image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:17.536758image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:23.896842image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:30.248983image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:35.117310image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:40.210551image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:45.576301image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:50.368460image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:55.994185image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:34:00.609190image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:34:05.479671image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:34:10.501251image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:31:44.198193image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:31:49.245785image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:31:55.275303image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:31:59.990359image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:04.960505image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:09.919545image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:14.981583image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:21.251172image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:27.348590image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:33.181679image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:39.699383image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:44.990687image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:50.410829image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:55.405199image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:01.884868image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:07.511862image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:12.311453image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:17.764579image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:24.151154image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:30.415044image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:35.275593image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:40.427005image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:45.753190image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:50.535213image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:56.161183image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:34:00.948758image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:34:05.656137image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:34:10.676980image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:31:44.370235image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:31:49.462886image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:31:55.440288image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:00.162577image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:05.139746image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:10.096398image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:15.202997image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:21.462784image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:27.523196image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:33.410133image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:39.917957image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:45.159737image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:50.575371image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:55.577155image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:02.101928image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:07.702314image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:12.488837image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:17.962985image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:24.379494image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:30.588837image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:35.441709image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:40.649926image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:45.922726image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:50.702180image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:56.328698image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:34:01.117235image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:34:05.828295image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:34:10.851987image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:31:44.542017image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:31:49.693664image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:31:55.607580image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:00.319448image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:05.309211image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:10.266918image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:15.416005image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:21.675369image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:27.695319image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:33.636706image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:40.150155image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:45.325321image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:50.739623image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:55.751658image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:02.319828image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:07.879829image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:12.676111image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:18.168201image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:24.620317image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:30.748594image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:35.601340image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:40.865670image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:46.086266image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:50.864029image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:56.488746image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:34:01.280002image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:34:05.993256image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:34:11.024871image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:31:44.707202image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:31:49.930708image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:31:55.778898image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:00.486677image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:05.480957image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:10.439967image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:15.634005image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:21.893336image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:27.867819image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:33.868071image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:40.378005image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:45.488712image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:50.904508image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:55.919765image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:02.533922image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:08.055134image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:12.856125image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:18.377878image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:24.865812image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:30.915256image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:35.766168image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:41.082911image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:46.249537image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:51.034687image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:56.649351image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:34:01.440378image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:34:06.153272image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:34:11.203882image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:31:44.877721image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:31:50.158663image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:31:55.950113image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:00.656995image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:05.662733image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:10.609961image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:15.852778image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:22.128455image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:28.041612image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:34.101755image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:40.605090image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:45.648413image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:51.079554image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:56.100048image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:02.744197image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:08.229876image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:13.045631image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:18.601734image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:25.108074image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:31.079864image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:35.928625image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:41.275979image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:46.419087image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:51.206460image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:56.808286image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:34:01.597659image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:34:06.311262image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:34:11.418356image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:31:45.051266image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:31:50.388477image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:31:56.120992image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:00.832415image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:05.852219image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:10.772969image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:16.071325image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:22.359105image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:28.217842image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:34.327930image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:40.834738image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:45.806426image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:51.292561image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:56.308089image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:02.948872image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:08.401289image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:13.269096image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:18.824509image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:25.352643image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:31.247336image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:36.095342image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:41.498743image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:46.760782image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:51.421133image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:56.968037image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:34:01.756010image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:34:06.478858image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:34:11.647675image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:31:45.226222image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:31:50.618359image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:31:56.295554image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:01.160464image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:06.037786image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:10.932350image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:16.280880image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:22.578989image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:28.406923image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:34.558156image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:41.040080image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:45.973943image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:51.508974image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:56.476683image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:03.155273image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:08.580052image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:13.454269image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:19.044168image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:25.584756image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:31.417638image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:36.255466image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:41.708844image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:46.929453image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:51.640443image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:57.125085image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:34:01.913014image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:34:06.650798image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:34:11.881559image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:31:45.396985image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:31:50.844300image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:31:56.464552image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:01.342048image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:06.204356image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:11.092999image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:16.663994image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:22.801230image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:28.637897image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:34.786269image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:41.211751image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:46.140571image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:51.719710image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:56.642900image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:03.363522image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:08.761787image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:13.630540image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:19.265280image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:25.823226image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:31.722181image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:36.422682image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:41.919367image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:47.093331image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:51.864875image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:57.264547image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:34:02.077294image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:34:06.821743image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:34:12.118133image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:31:45.567869image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:31:51.065634image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:31:56.631581image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:01.528841image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:06.367321image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:11.256755image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:16.881571image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:23.019251image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:28.885385image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:35.013450image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:41.382766image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:46.311304image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:51.932734image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:56.839536image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:03.575680image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:08.939661image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:13.794615image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:19.482401image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:26.065088image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:31.886811image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:36.580772image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:42.129743image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:47.258155image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:52.080302image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:57.420046image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:34:02.218962image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:34:06.986992image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:34:12.353502image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:31:45.741656image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:31:51.290033image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:31:56.796722image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:01.710551image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:06.538732image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:11.423425image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:17.100518image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:23.235924image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:29.116881image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:35.250678image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:41.558227image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:46.473661image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:52.147893image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:32:57.086023image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:03.778661image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:09.105395image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:14.135923image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:19.705367image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:26.302016image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:32.047611image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:36.741582image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:42.341130image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:47.415823image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:52.304700image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:33:57.587048image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:34:02.382102image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T19:34:07.147819image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-10-22T19:34:39.860628image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
countdiff_srv_ratedst_bytesdst_host_countdst_host_diff_srv_ratedst_host_rerror_ratedst_host_same_src_port_ratedst_host_same_srv_ratedst_host_serror_ratedst_host_srv_countdst_host_srv_diff_host_ratedst_host_srv_rerror_ratedst_host_srv_serror_ratedurationflaghotis_guest_loginlandlogged_innum_access_filesnum_compromisednum_failed_loginsnum_file_creationsnum_rootnum_shellsoutcomeprotocol_typererror_rateroot_shellsame_srv_rateserror_ratesrc_bytessrv_countsrv_diff_host_ratesrv_rerror_ratesrv_serror_ratesu_attemptedurgentwrong_fragment
count1.000-0.362-0.6390.547-0.547-0.2610.7770.540-0.3320.587-0.497-0.256-0.335-0.2590.318-0.1210.0720.0120.802-0.045-0.097-0.018-0.036-0.0550.0130.4670.725-0.2140.0200.347-0.3040.6660.951-0.384-0.221-0.3090.0050.0000.130
diff_srv_rate-0.3621.000-0.2230.1860.8510.299-0.727-0.8690.807-0.844-0.1770.2990.796-0.0510.081-0.0410.0090.0000.025-0.015-0.035-0.006-0.009-0.0110.0070.2500.1540.3460.003-0.9820.828-0.740-0.512-0.1380.3330.8150.0000.0000.000
dst_bytes-0.639-0.2231.000-0.612-0.035-0.003-0.3960.055-0.1670.0240.5790.002-0.1580.2990.0210.1930.0000.0000.0200.0660.1700.0220.034-0.0040.0000.3210.009-0.1010.0950.230-0.206-0.168-0.4980.521-0.081-0.1990.2040.0000.000
dst_host_count0.5470.186-0.6121.0000.044-0.1250.190-0.0700.1240.023-0.919-0.1250.114-0.1610.070-0.0740.0760.0310.680-0.024-0.042-0.028-0.050-0.0790.0250.2520.293-0.0680.026-0.1900.1650.1300.443-0.445-0.0730.1600.0130.0060.056
dst_host_diff_srv_rate-0.5470.851-0.0350.0441.0000.287-0.766-0.9800.719-0.955-0.0890.2710.7010.2320.143-0.0170.0390.0150.1100.027-0.039-0.0030.0270.0470.0020.2970.3530.3090.006-0.8450.720-0.713-0.674-0.0500.3000.7080.0000.0210.088
dst_host_rerror_rate-0.2610.299-0.003-0.1250.2871.000-0.299-0.278-0.088-0.3120.1150.951-0.112-0.0080.3320.1990.0140.0000.116-0.0050.2140.0320.001-0.0000.0040.2510.2290.9100.025-0.282-0.103-0.297-0.3130.0230.905-0.1230.0150.0000.214
dst_host_same_src_port_rate0.777-0.727-0.3960.190-0.766-0.2991.0000.771-0.6590.769-0.175-0.297-0.653-0.0650.230-0.0870.0460.0020.498-0.033-0.079-0.007-0.015-0.0160.0420.3460.698-0.2780.0160.733-0.6500.8150.812-0.223-0.282-0.6460.0010.0140.164
dst_host_same_srv_rate0.540-0.8690.055-0.070-0.980-0.2780.7711.000-0.7420.9700.108-0.264-0.725-0.2120.3080.0190.2620.0030.354-0.0210.0390.003-0.022-0.0390.0430.3530.530-0.3030.0100.874-0.7460.7290.6820.069-0.294-0.7340.0300.0160.193
dst_host_serror_rate-0.3320.807-0.1670.1240.719-0.088-0.659-0.7421.000-0.723-0.119-0.0970.968-0.0570.341-0.0050.0410.0310.193-0.011-0.0050.015-0.002-0.0080.0070.3620.416-0.0940.026-0.8300.974-0.646-0.449-0.098-0.0960.9670.0860.0000.115
dst_host_srv_count0.587-0.8440.0240.023-0.955-0.3120.7690.970-0.7231.0000.044-0.301-0.708-0.2170.306-0.0180.1600.0110.371-0.0240.003-0.015-0.027-0.0390.0390.3590.541-0.3300.0100.849-0.7240.7420.7210.035-0.323-0.7130.0160.0000.162
dst_host_srv_diff_host_rate-0.497-0.1770.579-0.919-0.0890.115-0.1750.108-0.1190.0441.0000.121-0.1040.1010.103-0.0140.0000.1200.0300.012-0.021-0.0030.0310.0610.0100.3410.0890.0730.0250.179-0.154-0.140-0.3920.4330.075-0.1480.0020.0000.153
dst_host_srv_rerror_rate-0.2560.2990.002-0.1250.2710.951-0.297-0.264-0.097-0.3010.1211.000-0.111-0.0140.3350.1890.0180.0000.111-0.0050.2180.032-0.001-0.0050.0000.1540.2230.9120.048-0.283-0.105-0.301-0.3080.0250.915-0.1250.0490.0000.008
dst_host_srv_serror_rate-0.3350.796-0.1580.1140.701-0.112-0.653-0.7250.968-0.708-0.104-0.1111.000-0.0570.375-0.0110.0200.2810.193-0.011-0.0050.015-0.004-0.0070.0250.3570.414-0.1110.054-0.8190.966-0.642-0.443-0.093-0.1140.9710.1710.0000.016
duration-0.259-0.0510.299-0.1610.232-0.008-0.065-0.212-0.057-0.2170.101-0.014-0.0571.0000.0800.1090.0270.0000.0110.0190.0110.0140.0610.0130.0000.0750.141-0.0260.0310.062-0.0740.014-0.2500.124-0.026-0.0740.0600.0000.000
flag0.3180.0810.0210.0700.1430.3320.2300.3080.3410.3060.1030.3350.3750.0801.0000.0090.0210.0140.2300.0080.0000.1170.0070.0000.0000.4650.4940.3440.0040.3220.3600.0410.2140.0440.3380.3690.0000.0000.019
hot-0.121-0.0410.193-0.074-0.0170.199-0.0870.019-0.005-0.018-0.0140.189-0.0110.1090.0091.0000.9730.0000.0910.0040.8120.1130.0290.0030.0160.1820.0340.0130.0270.041-0.0350.114-0.1150.0320.052-0.0350.0000.0000.000
is_guest_login0.0720.0090.0000.0760.0390.0140.0460.2620.0410.1600.0000.0180.0200.0270.0210.9731.0000.0000.0890.0000.0000.0000.0220.0000.0000.3710.0470.0090.0000.0220.0170.0000.0430.0070.0080.0170.0000.0000.000
land0.0120.0000.0000.0310.0150.0000.0020.0030.0310.0110.1200.0000.2810.0000.0140.0000.0001.0000.0020.0000.0000.0000.0000.0000.0000.9770.0080.0310.0000.0060.0280.0000.0060.0420.0000.0140.0000.0000.000
logged_in0.8020.0250.0200.6800.1100.1160.4980.3540.1930.3710.0300.1110.1930.0110.2300.0910.0890.0021.0000.0730.0080.0030.0150.0080.0240.8270.5280.1180.0250.2210.1930.0000.4810.4610.1430.2010.0120.0060.021
num_access_files-0.045-0.0150.066-0.0240.027-0.005-0.033-0.021-0.011-0.0240.012-0.005-0.0110.0190.0080.0040.0000.0000.0731.0000.0070.0060.0310.0150.0290.0610.027-0.0080.2770.015-0.014-0.001-0.0400.0240.002-0.0140.5800.0280.000
num_compromised-0.097-0.0350.170-0.042-0.0390.214-0.0790.039-0.0050.003-0.0210.218-0.0050.0110.0000.8120.0000.0000.0080.0071.0000.0050.0310.0290.0000.0000.0000.0090.2860.035-0.0310.119-0.0910.0360.054-0.0300.6450.0000.000
num_failed_logins-0.018-0.0060.022-0.028-0.0030.032-0.0070.0030.015-0.015-0.0030.0320.0150.0140.1170.1130.0000.0000.0030.0060.0051.0000.0150.0100.0000.4200.0100.0350.1350.006-0.004-0.008-0.018-0.0030.035-0.0040.2910.3330.000
num_file_creations-0.036-0.0090.034-0.0500.0270.001-0.015-0.022-0.002-0.0270.031-0.001-0.0040.0610.0070.0290.0220.0000.0150.0310.0310.0151.0000.0480.4340.1690.004-0.0050.2060.010-0.0100.028-0.0350.014-0.005-0.0100.0000.0000.000
num_root-0.055-0.011-0.004-0.0790.047-0.000-0.016-0.039-0.008-0.0390.061-0.005-0.0070.0130.0000.0030.0000.0000.0080.0150.0290.0100.0481.0000.0000.0000.000-0.0090.2860.014-0.016-0.002-0.0540.006-0.009-0.0160.6450.0000.000
num_shells0.0130.0070.0000.0250.0020.0040.0420.0430.0070.0390.0100.0000.0250.0000.0000.0160.0000.0000.0240.0290.0000.0000.4340.0001.0000.3690.0090.0000.2460.0070.0000.0000.0070.0000.0000.0000.0420.0000.000
outcome0.4670.2500.3210.2520.2970.2510.3460.3530.3620.3590.3410.1540.3570.0750.4650.1820.3710.9770.8270.0610.0000.4200.1690.0000.3691.0000.7560.2840.6000.3360.3580.0300.3560.1910.1930.3660.2040.2810.985
protocol_type0.7250.1540.0090.2930.3530.2290.6980.5300.4160.5410.0890.2230.4140.1410.4940.0340.0470.0080.5280.0270.0000.0100.0040.0000.0090.7561.0000.2230.0130.4860.4180.0000.7200.1940.2250.4150.0040.0010.152
rerror_rate-0.2140.346-0.101-0.0680.3090.910-0.278-0.303-0.094-0.3300.0730.912-0.111-0.0260.3440.0130.0090.0310.118-0.0080.0090.035-0.005-0.0090.0000.2840.2231.0000.015-0.328-0.091-0.342-0.281-0.0180.979-0.1110.0000.0000.008
root_shell0.0200.0030.0950.0260.0060.0250.0160.0100.0260.0100.0250.0480.0540.0310.0040.0270.0000.0000.0250.2770.2860.1350.2060.2860.2460.6000.0130.0151.0000.0110.0020.0000.0110.0000.0090.0020.3300.0950.000
same_srv_rate0.347-0.9820.230-0.190-0.845-0.2820.7330.874-0.8300.8490.179-0.283-0.8190.0620.3220.0410.0220.0060.2210.0150.0350.0060.0100.0140.0070.3360.486-0.3280.0111.000-0.8520.7440.5170.141-0.317-0.8390.0000.0000.030
serror_rate-0.3040.828-0.2060.1650.720-0.103-0.650-0.7460.974-0.724-0.154-0.1050.966-0.0740.360-0.0350.0170.0280.193-0.014-0.031-0.004-0.010-0.0160.0000.3580.418-0.0910.002-0.8521.000-0.657-0.428-0.121-0.0950.9910.0000.0000.171
src_bytes0.666-0.740-0.1680.130-0.713-0.2970.8150.729-0.6460.742-0.140-0.301-0.6420.0140.0410.1140.0000.0000.000-0.0010.119-0.0080.028-0.0020.0000.0300.000-0.3420.0000.744-0.6571.0000.723-0.104-0.333-0.6520.0000.0000.000
srv_count0.951-0.512-0.4980.443-0.674-0.3130.8120.682-0.4490.721-0.392-0.308-0.443-0.2500.214-0.1150.0430.0060.481-0.040-0.091-0.018-0.035-0.0540.0070.3560.720-0.2810.0110.517-0.4280.7231.000-0.239-0.284-0.4210.0000.0000.258
srv_diff_host_rate-0.384-0.1380.521-0.445-0.0500.023-0.2230.069-0.0980.0350.4330.025-0.0930.1240.0440.0320.0070.0420.4610.0240.036-0.0030.0140.0060.0000.1910.194-0.0180.0000.141-0.121-0.104-0.2391.0000.011-0.1120.0000.0000.121
srv_rerror_rate-0.2210.333-0.081-0.0730.3000.905-0.282-0.294-0.096-0.3230.0750.915-0.114-0.0260.3380.0520.0080.0000.1430.0020.0540.035-0.005-0.0090.0000.1930.2250.9790.009-0.317-0.095-0.333-0.2840.0111.000-0.1150.0000.0000.008
srv_serror_rate-0.3090.815-0.1990.1600.708-0.123-0.646-0.7340.967-0.713-0.148-0.1250.971-0.0740.369-0.0350.0170.0140.201-0.014-0.030-0.004-0.010-0.0160.0000.3660.415-0.1110.002-0.8390.991-0.652-0.421-0.112-0.1151.0000.0000.0000.016
su_attempted0.0050.0000.2040.0130.0000.0150.0010.0300.0860.0160.0020.0490.1710.0600.0000.0000.0000.0000.0120.5800.6450.2910.0000.6450.0420.2040.0040.0000.3300.0000.0000.0000.0000.0000.0000.0001.0000.0000.000
urgent0.0000.0000.0000.0060.0210.0000.0140.0160.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0060.0280.0000.3330.0000.0000.0000.2810.0010.0000.0950.0000.0000.0000.0000.0000.0000.0000.0001.0000.000
wrong_fragment0.1300.0000.0000.0560.0880.2140.1640.1930.1150.1620.1530.0080.0160.0000.0190.0000.0000.0000.0210.0000.0000.0000.0000.0000.0000.9850.1520.0080.0000.0300.1710.0000.2580.1210.0080.0160.0000.0001.000

Missing values

2024-10-22T19:34:12.857006image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-10-22T19:34:15.323831image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

durationprotocol_typeserviceflagsrc_bytesdst_byteslandwrong_fragmenturgenthotnum_failed_loginslogged_innum_compromisedroot_shellsu_attemptednum_rootnum_file_creationsnum_shellsnum_access_filesnum_outbound_cmdsis_host_loginis_guest_logincountsrv_countserror_ratesrv_serror_ratererror_ratesrv_rerror_ratesame_srv_ratediff_srv_ratesrv_diff_host_ratedst_host_countdst_host_srv_countdst_host_same_srv_ratedst_host_diff_srv_ratedst_host_same_src_port_ratedst_host_srv_diff_host_ratedst_host_serror_ratedst_host_srv_serror_ratedst_host_rerror_ratedst_host_srv_rerror_rateoutcome
00tcphttpSF18154500000010000000000880.00.00.00.01.00.00.0991.00.00.110.000.00.00.00.0normal.
10tcphttpSF2394860000010000000000880.00.00.00.01.00.00.019191.00.00.050.000.00.00.00.0normal.
20tcphttpSF23513370000010000000000880.00.00.00.01.00.00.029291.00.00.030.000.00.00.00.0normal.
30tcphttpSF21913370000010000000000660.00.00.00.01.00.00.039391.00.00.030.000.00.00.00.0normal.
40tcphttpSF21720320000010000000000660.00.00.00.01.00.00.049491.00.00.020.000.00.00.00.0normal.
50tcphttpSF21720320000010000000000660.00.00.00.01.00.00.059591.00.00.020.000.00.00.00.0normal.
60tcphttpSF21219400000010000000000120.00.00.00.01.00.01.01691.00.01.000.040.00.00.00.0normal.
70tcphttpSF15940870000010000000000550.00.00.00.01.00.00.011791.00.00.090.040.00.00.00.0normal.
80tcphttpSF2101510000010000000000880.00.00.00.01.00.00.08891.00.00.120.040.00.00.00.0normal.
90tcphttpSF2127860001010000000000880.00.00.00.01.00.00.08991.00.00.120.050.00.00.00.0normal.
durationprotocol_typeserviceflagsrc_bytesdst_byteslandwrong_fragmenturgenthotnum_failed_loginslogged_innum_compromisedroot_shellsu_attemptednum_rootnum_file_creationsnum_shellsnum_access_filesnum_outbound_cmdsis_host_loginis_guest_logincountsrv_countserror_ratesrv_serror_ratererror_ratesrv_rerror_ratesame_srv_ratediff_srv_ratesrv_diff_host_ratedst_host_countdst_host_srv_countdst_host_same_srv_ratedst_host_diff_srv_ratedst_host_same_src_port_ratedst_host_srv_diff_host_ratedst_host_serror_ratedst_host_srv_serror_ratedst_host_rerror_ratedst_host_srv_rerror_rateoutcome
4940110tcphttpSF30866200000100000000008100.000.000.00.01.00.00.30362551.00.00.030.060.000.010.00.0normal.
4940120tcphttpSF2911862000001000000000010110.000.000.00.01.00.00.18462551.00.00.020.050.000.010.00.0normal.
4940130tcphttpSF2892440000010000000000220.000.000.00.01.00.00.00562551.00.00.020.050.000.010.00.0normal.
4940140tcphttpSF3066620000010000000000220.000.000.00.01.00.00.00662551.00.00.020.050.000.010.00.0normal.
4940150tcphttpSF289186200000100000000004120.000.000.00.01.00.00.25762551.00.00.010.050.000.010.00.0normal.
4940160tcphttpSF31018810000010000000000450.000.000.00.01.00.00.40862551.00.00.010.050.000.010.00.0normal.
4940170tcphttpSF28222860000010000000000660.000.000.00.01.00.00.0062551.00.00.170.050.000.010.00.0normal.
4940180tcphttpSF203120000000100000000006180.170.110.00.01.00.00.17162551.00.00.060.050.060.010.00.0normal.
4940190tcphttpSF291120000000100000000006120.000.000.00.01.00.00.17262551.00.00.040.050.040.010.00.0normal.
4940200tcphttpSF219123400000100000000006350.000.000.00.01.00.00.1462551.00.00.170.050.000.010.00.0normal.

Duplicate rows

Most frequently occurring

durationprotocol_typeserviceflagsrc_bytesdst_byteslandwrong_fragmenturgenthotnum_failed_loginslogged_innum_compromisedroot_shellsu_attemptednum_rootnum_file_creationsnum_shellsnum_access_filesnum_outbound_cmdsis_host_loginis_guest_logincountsrv_countserror_ratesrv_serror_ratererror_ratesrv_rerror_ratesame_srv_ratediff_srv_ratesrv_diff_host_ratedst_host_countdst_host_srv_countdst_host_same_srv_ratedst_host_diff_srv_ratedst_host_same_src_port_ratedst_host_srv_diff_host_ratedst_host_serror_ratedst_host_srv_serror_ratedst_host_rerror_ratedst_host_srv_rerror_rateoutcome# duplicates
4730icmpecr_iSF1032000000000000000005115110.00.00.00.01.00.00.02552551.00.01.00.00.00.00.00.0smurf.193081
3660icmpecr_iSF520000000000000000005115110.00.00.00.01.00.00.02552551.00.01.00.00.00.00.00.0smurf.33368
4710icmpecr_iSF1032000000000000000005105100.00.00.00.01.00.00.02552551.00.01.00.00.00.00.00.0smurf.26185
4680icmpecr_iSF1032000000000000000005095090.00.00.00.01.00.00.02552551.00.01.00.00.00.00.00.0smurf.5194
4650icmpecr_iSF1032000000000000000005085080.00.00.00.01.00.00.02552551.00.01.00.00.00.00.00.0smurf.1292
3040icmpecr_iSF520000000000000000004494490.00.00.00.01.00.00.02552551.00.01.00.00.00.00.00.0smurf.944
3350icmpecr_iSF520000000000000000004804800.00.00.00.01.00.00.02552551.00.01.00.00.00.00.00.0smurf.833
3060icmpecr_iSF520000000000000000004514510.00.00.00.01.00.00.02552551.00.01.00.00.00.00.00.0smurf.819
3330icmpecr_iSF520000000000000000004784780.00.00.00.01.00.00.02552551.00.01.00.00.00.00.00.0smurf.773
3370icmpecr_iSF520000000000000000004824820.00.00.00.01.00.00.02552551.00.01.00.00.00.00.00.0smurf.699